setwd("~/OneDrive - University of Bristol/Winfred_PhDApps/Streamlit_MRevidenceentryapp/")#install.packages('RSQLite')
#install.packages("forestly")
#devtools::install_github("elong0527/forestly")
library(RSQLite)
#library(dplyr)
library(ggplot2)
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
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## last_plot## The following object is masked from 'package:stats':
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## filter## The following object is masked from 'package:graphics':
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## layoutlibrary(gridExtra)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.1 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ lubridate 1.9.2 ✔ tibble 3.2.1
## ✔ purrr 1.0.1 ✔ tidyr 1.3.0## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::combine() masks gridExtra::combine()
## ✖ dplyr::filter() masks plotly::filter(), stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(forestploter)##
## Attaching package: 'forestploter'
##
## The following object is masked from 'package:plotly':
##
## add_textlibrary(grid)
library(stringr)con <- dbConnect(SQLite(),'Mr_EvidenceDB')
#list tables
dbListTables(con) #list down the tables## [1] "effectsizetype" "exposure" "methods" "outcome"
## [5] "results" "study"result <- tbl(con, 'results')
results <- data.frame(result)
#exposure
exposure <- tbl(con, 'exposure')
exposure <- data.frame(exposure)
colnames(exposure) <- c("exposurename","exposureid")
exposure## exposurename exposureid
## 1 BMI E1
## 2 Adiposity E2
## 3 WHR E3
## 4 childhoodBMI E4
## 5 adultBMI E5
## 6 fatmassindex E6
## 7 fatfreemassindex E7
## 8 FA E8
## 9 UFA E9
## 10 obesity E10
## 11 bodyfat% E11
## 12 BMIWGRS E12
## 13 BMIGRS E13
## 14 WHtR E14
## 15 VAT E15#outcome
outcome <- tbl(con, 'outcome')
outcome <- data.frame(outcome)
colnames(outcome) <- c("outcomename","outcomeid") #rename the columns
outcome## outcomename outcomeid
## 1 incident hypertension O1
## 2 ever hypertension O2
## 3 hypertension O3
## 4 gestational hypertension O4
## 5 essential hypertension O5
## 6 SBP O6
## 7 DBP O7
## 8 PAP O8
## 9 PVremodelling O9
## 10 Grade1diastolicdysfunction O10
## 11 arterial hypertension O11#methods
methods <- tbl(con, 'methods')
methods <- data.frame(methods)
colnames(methods) <- c("methodname","methodid") #rename the columns
methods## methodname methodid
## 1 IVW M1
## 2 Wetmedian M2
## 3 Wetmode M3
## 4 MREgger M4
## 5 IVestimator M5
## 6 MVMR M6
## 7 penalisedwetmedian M7
## 8 SIMEXcorrectedMREgger M8
## 9 MRGXE M9
## 10 TSLS M10
## 11 MR-PRESSO M11#study
study <- tbl(con,'study')
study <- data.frame(study)
study## pmid
## 1 19470880
## 2 23824655
## 3 24462370
## 4 25411050
## 5 25712996
## 6 26568383
## 7 28196256
## 8 28317167
## 9 28678979
## 10 29935421
## 11 30045251
## 12 30462199
## 13 30707692
## 14 31195408
## 15 31501611
## 16 31708716
## 17 32636122
## 18 32665587
## 19 32712226
## 20 33045005
## 21 33131310
## 22 33323262
## 23 33771188
## 24 33980691
## 25 34001814
## 26 34002035
## 27 34120448
## 28 34322150
## 29 34333589
## 30 34465205
## 31 35074047
## 32 35186940
## 33 35232963
## 34 35599089
## 35 35656995
## 36 35694671
## 37 35947639
## title
## 1 Does Greater Adiposity Increase Blood Pressure and Hypertension Risk?
## 2 The Role of Adiposity in Cardiometabolic Traits: A Mendelian Randomization Analysis
## 3 Causal Effects of Body Mass Index on Cardiometabolic Traits and Events: A Mendelian Randomization Analysis
## 4 Remnant cholesterol, low-density lipoprotein cholesterol, and blood pressure as mediators from obesity to ischemic heart disease
## 5 Age- and Sex-Specific Causal Effects of Adiposity on Cardiovascular Risk Factors
## 6 MR-PheWAS: hypothesis prioritization among potential causal effects of body mass index on many outcomes, using Mendelian randomization
## 7 Genetic Association of Waist-to-Hip Ratio With Cardiometabolic Traits, Type 2 Diabetes, and Coronary Heart Disease
## 8 Semiparametric methods for estimation of a nonlinear exposure-outcome relationship using instrumental variables with application to Mendelian randomization
## 9 Association of Body Mass Index With Cardiometabolic Disease in the UK Biobank: A Mendelian Randomization Study
## 10 Mendelian randomisation in type 2 diabetes and coronary artery disease
## 11 Causal association of body mass index with hypertension using a Mendelian randomization design
## 12 Detecting and correcting for bias in Mendelian randomization analyses using Gene-by- Environment interactions
## 13 Searching for the causal effects of body mass index in over 300 000 participants in UK Biobank, using Mendelian randomization
## 14 Body mass index and body composition in relation to 14 cardiovascular conditions in UK Biobank: a Mendelian randomization study
## 15 Contribution of genetics to visceral adiposity and its relation to cardiovascular and metabolic disease
## 16 On the Use of the Lasso for Instrumental Variables Estimation with Some Invalid Instruments
## 17 Causal associations of body mass index and waist-to-hip ratio with cardiometabolic traits among Chinese children: A Mendelian randomization study
## 18 Avoiding dynastic, assortative mating, and population stratification biases in Mendelian randomization through within-family analyses
## 19 BMI Is Causally Associated With Pulmonary Artery Pressure But Not Hemodynamic Evidence of Pulmonary Vascular Remodeling
## 20 Novel loci for childhood body mass index and shared heritability with adult cardiometabolic traits
## 21 Association of Cardiovascular Risk Factors and Lifestyle Behaviors With Hypertension A Mendelian Randomization Study
## 22 A data-driven approach for studying the role of body mass in multiple diseases: a phenome-wide registry-based case-control study in the UK Biobank
## 23 Phenome-wide investigation of the causal associations between childhood BMI and adult trait outcomes: a two-sample Mendelian randomization study
## 24 Genetic Evidence for Different Adiposity Phenotypes and Their Opposing Influences on Ectopic Fat and Risk of Cardiometabolic Disease
## 25 Birth weight, childhood obesity and risk of hypertension: a Mendelian randomization study
## 26 Risk factors mediating the effect of body mass index and waist-to-hip ratio on cardiovascular outcomes: Mendelian randomization analysis
## 27 Causal Effect of Adiposity Measures on Blood Pressure Traits in 2 Urban Swedish Cohorts: A Mendelian Randomization Study
## 28 A Novel Method for Mendelian Randomization Analyses With Pleiotropy and Linkage Disequilibrium in Genetic Variants From Individual Data
## 29 Association between adiposity and cardiovascular outcomes: an umbrella review and meta-analysis of observational and Mendelian randomization studies
## 30 Mendelian Randomization Analyses Suggest Childhood Body Size Indirectly Influences End Points From Across the Cardiovascular Disease Spectrum Through Adult Body Size
## 31 Disease consequences of higher adiposity uncoupled from its adverse metabolic effects using Mendelian randomisation
## 32 Associations of Visceral Adipose Tissue, Circulating Protein Biomarkers, and Risk of Cardiovascular Diseases: A Mendelian Randomization Analysis
## 33 Robust Mendelian randomization in the presence of residual population stratification, batch effects and horizontal pleiotropy
## 34 Distinct causal effects of body fat distribution on cardiometabolic traits among children: Findings from the BCAMS study
## 35 Genetic Determinants of Body Mass Index and Fasting Glucose Are Mediators of Grade 1 Diastolic Dysfunction
## 36 Genetically Predicted Obesity Causally Increased the Risk of Hypertension Disorders in Pregnancy
## 37 Interaction-based Mendelian randomization with measured and unmeasured gene-by- covariate interactions
## population sex mean_age median_age lower_age upper_age year samplesize
## 1 EUR both 0.00 0.00 0 0.00 2009.00 37027
## 2 EUR both 0.00 0.00 0 0.00 2013.00 198502
## 3 EUR both 0.00 0.00 0 0.00 2014.00 34540
## 4 EUR both 0.00 0.00 0 0.00 2015.00 65650
## 5 EUR both 0.00 0.00 0 55.00 2014.97 67553
## 6 EUR both 0.00 0.00 0 0.00 2015.00 8121
## 7 EUR female 57.00 0.00 0 0.00 2017.00 111986
## 8 EUR both 0.00 0.00 0 0.00 2017.00 133687
## 9 EUR both 56.87 0.00 0 0.00 2017.00 119859
## 10 EUR both 0.00 0.00 0 0.00 2018.00 0
## 11 Korean both 0.00 0.00 40 69.00 2018.00 8832
## 12 EUR both 0.00 0.00 0 0.00 2018.00 0
## 13 EUR both 0.00 0.00 0 0.00 2019.00 334968
## 14 EUR both 57.20 0.00 40 68.96 2019.00 367703
## 15 EUR both 0.00 0.00 0 0.00 2019.00 0
## 16 EUR both 0.00 0.00 0 0.00 2018.00 0
## 17 East Asian both 0.00 0.00 7 18.00 2020.00 2030
## 18 EUR both 0.00 0.00 0 0.00 2020.00 61008
## 19 EUR both 0.00 0.00 0 0.00 2021.00 1043
## 20 EUR both 0.00 0.00 2 9.97 2020.00 61111
## 21 EUR both 0.00 0.00 0 0.00 2020.00 0
## 22 EUR both 0.00 0.00 37 73.00 2016.00 337536
## 23 EUR both 0.00 0.00 2 10.00 2021.00 0
## 24 EUR both 0.00 0.00 0 0.00 2021.00 0
## 25 EUR both 0.00 0.00 0 0.00 2020.00 0
## 26 EUR both 0.00 0.00 0 0.00 2021.00 898130
## 27 EUR both 0.00 0.00 0 0.00 2021.00 27000
## 28 EUR both 0.00 0.00 0 0.00 2021.00 0
## 29 EUR both 0.00 0.00 0 0.00 2021.00 0
## 30 EUR both 0.00 0.00 0 0.00 2021.00 0
## 31 EUR both 0.00 0.00 0 0.00 2022.00 0
## 32 EUR both 0.00 0.00 0 0.00 2022.00 0
## 33 EUR both 0.00 0.00 0 0.00 2022.00 0
## 34 East Asian both 12.41 0.00 0 0.00 0.00 3266
## 35 EUR both 47.20 -0.03 0 60.00 2021.96 2440
## 36 EUR female 0.00 0.00 0 0.00 2022.00 0
## 37 EUR both 0.00 0.00 0 0.00 2022.00 100000
## author
## 1 Nicholas J. Timpson, Roger Harbord, George Davey Smith, Jeppe Zacho, Anne Tybjærg-Hansen, Børge G. Nordestgaard
## 2 European Network for Genetic and Genomic Epidemiology (ENGAGE) consortium
## 3 Michael V. Holmes,1,2,* Leslie A. Lange,3 Tom Palmer,4 Matthew B. Lanktree,5 Kari E. North,6 Berta Almoguera,7 Sarah Buxbaum,8 Hareesh R. Chandrupatla,7 Clara C. Elbers,9 Yiran Guo,7 Ron C. Hoogeveen,10 Jin Li,7 Yun R. Li,7 Daniel I. Swerdlow,2 Mary Cushman,11 Tom S. Price,12 Sean P. Curtis,13 Myriam Fornage,14 Hakon Hakonarson,7 Sanjay R. Patel,15 Susan Redline,15 David S. Siscovick,16 Michael Y. Tsai,17 James G. Wilson,18 Yvonne T. van der Schouw,19 Garret A. FitzGerald,12 Aroon D. Hingorani,2 Juan P. Casas,2,20 Paul I.W. de Bakker,19,21 Stephen S. Rich,22 Eric E. Schadt,23 Folkert W. Asselbergs,24,25,26 Alex P. Reiner,22,27 and Brendan J. Keating1,7,28,*
## 4 Anette Varbo 1 , Marianne Benn 1 , George Davey Smith 1 , Nicholas J Timpson 1 , Anne Tybjaerg-Hansen 1 , Børge G Nordestgaard 2
## 5 Tove Fall Engage Consortium
## 6 Louise A. C. Millard1,2, Neil M. Davies1, Nic J. Timpson1, Kate Tilling1, Peter A. Flach1,2 & George Davey Smith
## 7 Emdin CA, Khera AV, Natarajan P, Klarin D, Zekavat SM, Hsiao AJ, Kathiresan S.
## 8 James R Staley 1 , Stephen Burgess 1
## 9 Donald M. Lyall, PhD; Carlos Celis-Morales, PhD; Joey Ward, BSc; Stamatina Iliodromiti, MD;Jana J. Anderson, PhD; Jason M. R. Gill, PhD; Daniel J. Smith, PhD; Uduakobong Efanga Ntuk, PhD, MSc;Daniel F. Mackay, PhD; Michael V. Holmes, MD, PhD; Naveed Sattar, MD, PhD; Jill P. Pell, MD
## 10 Timothy M Frayling 1 , Charli E Stoneman 1
## 11 Mee-Ri Lee, MD, PhDa, Youn-Hee Lim, PhDb,c, Yun-Chul Hong, MD, PhD
## 12 Wes Spiller,1* David Slichter,2 Jack Bowden1† and George Davey Smith
## 13 Louise A. C. MillardID1,2,3*, Neil M. DaviesID1,2, Kate TillingID1,2, Tom R. GauntID1,2, George Davey SmithID1,2
## 14 Susanna C. Larsson 1,2*, Magnus Ba Ãàck3,4, Jessica M.B. Rees 5,6, Amy M. Mason;Stephen Burgess
## 15 Torgny Karlsson 1 , Mathias Rask-Andersen 2 , Gang Pan 2 , Julia Höglund 2 , Claes Wadelius 2 , Weronica E Ek 2 , Åsa Johansson 3
## 16 Frank Windmeijer 1 2 , Helmut Farbmacher 3 , Neil Davies 2 4 , George Davey Smith 2
## 17 QiyingSongaTaoHuangbJieyunSongcXiangruiMengdChenxiongLiaYanWangaHaijunWanga
## 18 Ben Brumpton 1 2 3 , Eleanor Sanderson 4 5 , Karl Heilbron 6 , Fernando Pires Hartwig 4 7 , Sean Harrison 4 5 , Gunnhild Åberge Vie 8 , Yoonsu Cho 4 5 , Laura D Howe 4 5 , Amanda Hughes 4 5 , Dorret I Boomsma 9 , Alexandra Havdahl 4 10 11 , John Hopper 12 , Michael Neale 13 , Michel G Nivard 9 , Nancy L Pedersen 14 , Chandra A Reynolds 15 , Elliot M Tucker-Drob 16 , Andrew Grotzinger 16 , Laurence Howe 4 5 , Tim Morris 4 5 , Shuai Li 12 17 , Within-family Consortium; 23andMe Research Team; Adam Auton 6 , Frank Windmeijer 4 18 , Wei-Min Chen 19 , Johan Håkon Bjørngaard 8 20 , Kristian Hveem 8 , Cristen Willer 21 22 23 , David M Evans 4 24 , Jaakko Kaprio 25 26 , George Davey Smith # 4 5 , Bjørn Olav Åsvold # 8 27 , Gibran Hemani # 4 5 , Neil M Davies # 28 29 30
## 19 Timothy E. Thayer, MD; Rebecca T. Levinson, PhD; Shi Huang, PhD; Tufik Assad, MD; Eric Farber-Eger, BS; Quinn S. Wells, MD; Jonathan D. Mosley, MD, PhD; and Evan L. Brittain, MD
## 20 Suzanne Vogelezang1,2,3‡, Jonathan P. Bradfield4,5‡, Tarunveer S. Ahluwalia6,7,8,9, John A. Curtin10, Timo A. Lakka11,12,13, Niels Grarup8, Markus Scholz14,15, Peter J. van der Most16, Claire Monnereau1,2,3, Evie Stergiakouli17,18,19, Anni Heiskala20, Momoko Horikoshi21,22,23, Iryna O. Fedko24, Natalia Vilor-Tejedor25,26,27,28, Diana L. Cousminer29,30, Marie Standl31, Carol A. Wang32, Jorma Viikari33,34, Frank Geller35, Carmen ́Iñiguez28,36,37, Niina Pitka ̈ nen38,39, Alessandra Chesi29, Jonas Bacelis40,41,
## 21 Sabine van Oort , Joline W.J. Beulens, Adriana J. van Ballegooijen, Diederick E. Grobbee, Susanna C. Larsson
## 22 Elina Hyppönen 1 , Anwar Mulugeta 2 , Ang Zhou 3 , Vimaleswaran Karani Santhanakrishnan 4
## 23 Shan-Shan Dong1†, Kun Zhang1†, Yan Guo1, Jing-Miao Ding1, Yu Rong1, Jun-Cheng Feng1, Shi Yao1, Ruo-Han Hao1, Feng Jiang1, Jia-Bin Chen1, Hao Wu1, Xiao-Feng Chen1 and Tie-Lin Yang
## 24 Susan Martin 1 , Madeleine Cule 2 , Nicolas Basty 3 , Jessica Tyrrell 1 , Robin N Beaumont 1 , Andrew R Wood 1 , Timothy M Frayling 1 , Elena Sorokin 2 , Brandon Whitcher 3 , Yi Liu 2 , Jimmy D Bell 3 , E Louise Thomas 3 , Hanieh Yaghootkar
## 25 Jingwen Fan 1 , Xuezhong Shi, Xiaocan Jia, Yuping Wang, Yang Zhao, Junzhe Bao, Haomin Zhang, Yongli Yang
## 26 Dipender Gill 1 2 3 4 , Verena Zuber 5 6 7 , Jesse Dawson 8 , Jonathan Pearson-Stuttard 5 7 , Alice R Carter 9 10 , Eleanor Sanderson 9 10 , Ville Karhunen 5 , Michael G Levin 11 12 13 , Robyn E Wootton 9 14 15 , Derek Klarin 16 17 18 19 , Philip S Tsao 20 21 , Konstantinos K Tsilidis 5 22 , Scott M Damrauer 13 23 , Stephen Burgess 6 24 , Paul Elliott 5 7 25 26 27
## 27 Alice Giontella 1 2 , Luca A Lotta 3 , John D Overton 3 , Aris Baras 3 , Regeneron Genetics Center; Pietro Minuz 1 , Olle Melander 2 4 , Dipender Gill 5 6 , Cristiano Fava 1 2
## 28 Yuquan Wang 1 , Tingting Li 1 , Liwan Fu 1 , Siqian Yang 1 , Yue-Qing Hu 1 2
## 29 Min Seo Kim 1 2 , Won Jun Kim 1 3 , Amit V Khera 4 5 6 , Jong Yeob Kim 7 , Dong Keon Yon 8 , Seung Won Lee 9 , Jae Il Shin 10 , Hong-Hee Won 2 11
## 30 Grace M Power 1 , Jessica Tyrrell 2 , Timothy M Frayling 2 , George Davey Smith 1 , Tom G Richardson 1 3
## 31 Susan Martin 1 , Jessica Tyrrell 1 , E Louise Thomas 2 , Matthew J Bown 3 4 , Andrew R Wood 1 , Robin N Beaumont 1 , Lam C Tsoi 5 , Philip E Stuart 5 , James T Elder 5 6 , Philip Law 7 , Richard Houlston 7 , Christopher Kabrhel 8 9 , Nikos Papadimitriou 10 , Marc J Gunter 10 , Caroline J Bull 11 12 13 , Joshua A Bell 11 12 , Emma E Vincent 11 12 13 , Naveed Sattar 14 , Malcolm G Dunlop 15 16 , Ian P M Tomlinson 17 , Sara Lindström 18 19 , INVENT consortium; Jimmy D Bell 2 , Timothy M Frayling 1 , Hanieh Yaghootkar
## 32 Yunying Huang 1 , Yaozhong Liu 1 , Yingxu Ma 1 , Tao Tu 1 , Na Liu 1 , Fan Bai 2 , Yichao Xiao 1 , Chan Liu 3 , Zhengang Hu 1 , Qiuzhen Lin 1 , Mohan Li 1 , Zuodong Ning 1 , Yong Zhou 1 , Xiquan Mao 1 , Qiming Liu 1
## 33 Carlos Cinelli 1 , Nathan LaPierre 2 , Brian L Hill 2 , Sriram Sankararaman 2 3 4 , Eleazar Eskin 2 3 4
## 34 Liwan Fu 1 , Hong Cheng 2 , Xiaoyuan Zhao 2 , Dongqing Hou 2 , Xianghui Xie 3 , Jie Mi 4
## 35 Nataraja Sarma Vaitinadin , MBBS, PhD, MPH; Mingjian Shi , MD, PhD; Christian M. Shaffer, BS; Eric Farber-Eger , BS; Brandon D. Lowery, BS; Vineet Agrawal, MD, PhD; Deepak K. Gupta , MD, MSCI; Dan M. Roden , MD; Quinn S. Wells , MD, PharmD, MSCI, MSc; Jonathan D. Mosley , MD, PhD
## 36 Wenting Wang1,2†, Jiang-Shan Tan3†, Lu Hua3, Shengsong Zhu3, Hongyun Lin2, Yan Wu3* and Jinping Liu
## 37 Wes Spiller 1 , Fernando Pires Hartwig 2 , Eleanor Sanderson 1 , George Davey Smith 1 , Jack Bowden 3#effectsizetype
effectsizetype <- tbl(con,'effectsizetype')
effectsizetype <- data.frame(effectsizetype)
effectsizetype## id effectsizetype
## 1 ID1 OR
## 2 ID2 MD
## 3 ID3 HR
## 4 ID4 BETA
## 5 ID5 RiskDifference#merging two dataframes results and exposure
results_exposure <- merge(results, exposure,by.x = "exposureid",by.y = "exposureid")
#merge the above dataframe and outcome
results_exposure_outcome <- merge(results_exposure,outcome, by.x = "outcomeid",by.y = "outcomeid")
#merge with study table
results_exposure_outcome_study <- merge(results_exposure_outcome,study, by.x = "pmid",by.y = "pmid")
#merge the above with methods
results_exposure_outcome_study_method <- merge(results_exposure_outcome_study,methods, by.x = 'methodid',by.y='methodid')
#merge the above with effectsizetype table
results_exposure_outcome_study_method_effectsizetype <- merge(results_exposure_outcome_study_method, effectsizetype,by.x = 'effectsizetype_id',by.y = 'id')
#reorder the columns
colnames(results_exposure_outcome_study_method_effectsizetype)## [1] "effectsizetype_id" "methodid" "pmid"
## [4] "outcomeid" "exposureid" "results_id"
## [7] "effectsize" "lowerinterval" "upperinterval"
## [10] "pvalue" "standard_error" "strata"
## [13] "exposurename" "outcomename" "title"
## [16] "population" "sex" "mean_age"
## [19] "median_age" "lower_age" "upper_age"
## [22] "year" "samplesize" "author"
## [25] "methodname" "effectsizetype"mydata <- results_exposure_outcome_study_method_effectsizetype[,c(3,6,15,24,22,16,23,17,18,19,20,21,
5,13,4,14,2,25,1,26,7,8,9,10,11,12)]
#Split the author column into two
mydata[c('first_author','last_author')] <- str_split_fixed(mydata$author,pattern = ',',2)
#combine pmid_first_author_exposure to be used as unique identifiers
mydata$UID <- paste(mydata$pmid,mydata$first_author,mydata$exposurename, sep = "_")
#mydata
#write out the data to a csv file
#write.table(mydata, file = "Obesity_HBP_MR_evidence.csv",sep = ",")####Looking at systolic blood pressure
#Systolic blood pressure----
mydata$effectsize[(mydata$effectsize==10.43)] <- 1.048
mydata$lowerinterval[(mydata$lowerinterval==4.82)] <- 0.482
mydata$upperinterval[(mydata$upperinterval==16.04)] <- 1.604
mydata$effectsize[(mydata$effectsize==3.85)] <- 0.385
mydata$lowerinterval[(mydata$lowerinterval==1.88)] <- 0.188
mydata$upperinterval[(mydata$upperinterval==5.79)] <- 0.579
#Diastolic blood pressure----
mydata$effectsize[(mydata$effectsize==2.97)] <- 0.297
mydata$lowerinterval[(mydata$lowerinterval==-0.08)] <- -0.008
mydata$upperinterval[(mydata$upperinterval==6.03)] <- 0.603
mydata$effectsize[(mydata$effectsize==1.79)] <- 0.179
mydata$lowerinterval[(mydata$lowerinterval==0.680)] <- 0.068
mydata$upperinterval[(mydata$upperinterval==2.90)] <- 0.290
#modify Louise Millard results
mydata$exposurename[mydata$exposurename == 'BMI' & mydata$effectsize == 0.29 & mydata$pmid == 26568383] <- "BMIscore32"
mydata$exposurename[mydata$exposurename == 'BMI' & mydata$effectsize == 0.385 & mydata$pmid == 26568383] <- 'BMIscoreFTO'
mydata$exposurename[mydata$exposurename == 'BMI' & mydata$effectsize == 0.305 & mydata$pmid == 26568383] <- "logBMIscore"
p1 <- mydata %>%
#group_by(pmid) %>%
filter(effectsizetype_id=="ID2" &outcomeid=="O6") %>%
ggplot(aes(y=UID,x=effectsize,xmin=lowerinterval,xmax=upperinterval)) +
geom_point(aes(color=exposurename)) +
geom_errorbarh(height=.2) +
labs(title='Forest plot based on mean difference', x='Systolic blood pressure change in mmHg', y = 'StudyPMID_Author_exposure') +
geom_vline(xintercept=0, color='black', linetype='dashed', alpha=.5) +
facet_grid(outcomename ~ exposurename)+
theme_classic()
ggplotly(p1)p2 <- mydata %>%
#group_by(pmid) %>%
filter(effectsizetype_id=="ID2" &outcomeid=="O7") %>%
ggplot(aes(y=UID,x=effectsize,xmin=lowerinterval,xmax=upperinterval)) +
geom_point(aes(color=exposurename)) +
geom_errorbarh(height=.2) +
labs(title='Forest plot based on mean difference', x='Diastolic blood pressure change in mmHg', y = 'StudyPMID_Author_exposure') +
geom_vline(xintercept=0, color='black', linetype='dashed', alpha=.5) +
facet_grid(outcomename ~ exposurename)+
theme_classic()
ggplotly(p2)#Diastolic blood pressure----
P3 <- mydata %>%
#group_by(pmid) %>%
filter(effectsizetype_id=="ID5" & outcomeid=="O7") %>%
ggplot(aes(y=UID,x=effectsize,xmin=lowerinterval,xmax=upperinterval)) +
geom_point(aes(color=outcomename)) +
geom_errorbarh(height=.2) +
labs(title='Forest plot based on risk difference', x='Risk difference', y = 'StudyPMID_Author_exposure') +
geom_vline(xintercept=0, color='black', linetype='dashed', alpha=.5) +
#facet_wrap(vars(outcomename))+
theme_classic()
ggplotly(P3)#Risk diference
#Hypertension----
#Rename the exposures
mydata$exposurename[mydata$exposurename == 'BMI' & mydata$effectsize == 0.83 & mydata$pmid == 32665587] <- "BMIsiblings"
mydata$exposurename[mydata$exposurename == 'BMI' & mydata$effectsize == 0.76 & mydata$pmid == 32665587] <- 'BMIsiblingsplitsample'
P4 <- mydata %>%
#group_by(pmid) %>%
filter(effectsizetype_id=="ID5" & outcomeid=="O3") %>%
ggplot(aes(y=UID,x=effectsize,xmin=lowerinterval,xmax=upperinterval)) +
geom_point(aes(color=exposurename)) +
geom_errorbarh(height=.2) +
labs(title='Effect Size by study', x='Risk difference', y = 'StudyPMID_Author_exposure') +
geom_vline(xintercept=0, color='black', linetype='dashed', alpha=.5) +
facet_grid(exposurename ~ outcomename)+
theme_classic()
ggplotly(P4)#Beta estimates—-
#SBP----
P5 <- mydata %>%
#group_by(pmid) %>%
filter(effectsizetype_id=="ID4" & outcomeid== c("O6","O7") & population == 'EUR') %>%
ggplot(aes(y=UID,x=effectsize,xmin=lowerinterval,xmax=upperinterval)) +
geom_point(aes(color=exposurename)) +
geom_errorbarh(height=.2) +
labs(title='Forest plot based on beta estimates', x='Beta', y = 'StudyPMID_Author_exposure') +
geom_vline(xintercept=0, color='black', linetype='dashed', alpha=.5) +
facet_grid(exposurename ~ outcomename)+
theme_classic()## Warning: There was 1 warning in `filter()`.
## ℹ In argument: `&...`.
## Caused by warning in `outcomeid == c("O6", "O7")`:
## ! longer object length is not a multiple of shorter object lengthggplotly(P5)#DBP----
#Improve this plot
P6 <- mydata %>%
#group_by(pmid) %>%
filter(effectsizetype_id=="ID4" & outcomeid=="O7") %>%
ggplot(aes(y=UID,x=effectsize,xmin=lowerinterval,xmax=upperinterval)) +
geom_point(aes(color=exposurename)) +
geom_errorbarh(height=.2) +
labs(title='Effect Size by study', x='Beta', y = 'StudyPMID_Author_exposure') +
geom_vline(xintercept=0, color='black', linetype='dashed', alpha=.5) +
#facet_wrap(vars(outcomename))+
theme_classic()
ggplotly(P6)#Pulmonary arterial hypertension----
class(results_exposure_outcome$standard_error)## [1] "numeric"P7 <- mydata %>%
#group_by(pmid) %>%
filter(effectsizetype_id=="ID4" & outcomeid=="O8") %>%
ggplot(aes(y=UID,x=effectsize,xmin=lowerinterval,xmax=upperinterval))+
geom_point(aes(color=exposurename)) +
geom_errorbarh(height=.2) +
labs(title='Forest plot based on beta estimates', x='Beta', y = 'StudyPMID_Author_exposure') +
geom_vline(xintercept=0, color='black', linetype='dashed', alpha=.5) +
#facet_wrap(vars(outcomename))+
theme_classic()
ggplotly(P7)#Odds ratios
#Hypertension----
class(mydata$standard_error)## [1] "numeric"P8 <- mydata %>%
#group_by(pmid) %>%
filter(effectsizetype_id=="ID1" & outcomeid=="O3") %>%
ggplot(aes(y=UID,x=effectsize,xmin=lowerinterval,xmax=upperinterval))+
geom_point(aes(color=exposurename)) +
geom_errorbarh(height=.2) +
labs(title='Forest plot based on odds ratios', x='Odds ratio', y = 'StudyPMID_Author_exposure') +
geom_vline(xintercept=1, color='black', linetype='dashed', alpha=.5) +
#facet_wrap(vars(outcomename))+
theme_classic()
ggplotly(P8)#SBP----
class(mydata$standard_error)## [1] "numeric"P9 <- mydata %>%
#group_by(pmid) %>%
filter(effectsizetype_id=="ID1" & outcomeid=="O6") %>%
ggplot(aes(y=UID,x=effectsize,xmin=lowerinterval,xmax=upperinterval))+
geom_point(aes(color=exposurename)) +
geom_errorbarh(height=.2) +
labs(title='Forest plot based on odds ratio', x='Odds ratio', y = 'StudyPMID_Author_exposure') +
geom_vline(xintercept=1, color='black', linetype='dashed', alpha=.5) +
facet_grid(exposurename ~ outcomename)+
theme_classic()
ggplotly(P9)#DBP----
class(results_exposure_outcome$standard_error)## [1] "numeric"P10 <- mydata %>%
#group_by(pmid) %>%
filter(effectsizetype_id=="ID1" & outcomeid=="O7") %>%
ggplot(aes(y=UID,x=effectsize,xmin=lowerinterval,xmax=upperinterval))+
geom_point(aes(color=methodname)) +
geom_errorbarh(height=.2) +
labs(title='Forest plot based on odds ratio', x='Odds ratio', y = 'StudyPMID_Author_exposure') +
geom_vline(xintercept=1, color='black', linetype='dashed', alpha=.5) +
facet_wrap(exposurename ~ methodname)+
theme_classic()
ggplotly(P10)#essentialhypertension----
class(results_exposure_outcome$standard_error)## [1] "numeric"P11 <- mydata %>%
#group_by(pmid) %>%
filter(effectsizetype_id=="ID1" & outcomeid=="O5") %>%
ggplot(aes(y=UID,x=effectsize,xmin=lowerinterval,xmax=upperinterval))+
geom_point(aes(color=methodname)) +
geom_errorbarh(height=.2) +
labs(title='Forest plot based on odds ratio', x='Odds ratio', y = 'Results_ID') +
geom_vline(xintercept=1, color='black', linetype='dashed', alpha=.5) +
facet_wrap(exposurename ~ methodname)+
theme_classic()
ggplotly(P11)